Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/26406
Title: Energy Optimization for IRS-Aided SWIPT under Imperfect Cascaded Channels
Authors: Zhang, C
Huang, Y
He, C
Pan, C
Wang, K
Keywords: intelligent reflecting surface;simultaneous wire-less information and power transfer;max-min energy
Issue Date: 11-Apr-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: Zhang, C. et al. (2023) 'Energy Optimization for IRS-Aided SWIPT under Imperfect Cascaded Channels', IEEE Transactions on Vehicular Technology, 72 (9), pp. 11631 - 11643. doi: 10.1109/tvt.2023.3265963.
Abstract: In this paper, intelligent reflecting surface is deployed in simultaneous wireless information and power transfer (SWIPT) system to improve the energy harvesting performance. We investigate the robust beamforming design considering the impact of the imperfect cascaded channels. We maximize the minimum received energy among all energy receivers to ensure fairness, while guaranteeing the worst-case information receivers rate requirement. To address with the coupling effect of multiple variables in the optimization problem, the alternate optimization method is utilized to decouple the optimization problem into two sub-problems, and the successive convex approximation and the semidefinite relaxation methods are used to solve the sub-problems. Simulation results reveal that employing IRS into SWIPT system can enhance the energy harvest performance, Additionally, our proposed two algorithms converges rapidly and can guarantee the robustness of the system.
URI: https://bura.brunel.ac.uk/handle/2438/26406
DOI: https://doi.org/10.1109/tvt.2023.3265963
ISSN: 0018-9545
Other Identifiers: ORCID iDs: Chiya Zhang https://orcid.org/0000-0002-1113-4659
ORCID iD: Yin Huang https://orcid.org/0000-0002-7106-1498
ORCID iD: Chunlong He https://orcid.org/0000-0003-4316-0672
ORCID iD: Cunhua Pan https://orcid.org/0000-0001-5286-7958
ORCID iD: Kezhi Wang https://orcid.org/0000-0001-8602-0800
Appears in Collections:Dept of Computer Science Research Papers

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